Difference between revisions of "Machine Learning Bio"
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* [http://www-bcf.usc.edu/~gareth/ISL/ An Introduction to Statistical Learning with Applications in R] by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani | * [http://www-bcf.usc.edu/~gareth/ISL/ An Introduction to Statistical Learning with Applications in R] by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani | ||
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The course is composed by a set of ex-cattedra lectures on specific techniques (e.g., linear regression, linear discriminant analysis, clustering, etc.). Supervised and unsupervised learning are discussed in the framework of classification and clustering problems. The course outline is: | The course is composed by a set of ex-cattedra lectures on specific techniques (e.g., linear regression, linear discriminant analysis, clustering, etc.). Supervised and unsupervised learning are discussed in the framework of classification and clustering problems. The course outline is: | ||
Revision as of 00:15, 11 March 2020
The following are last minute news you should be aware of ;-)
* 11/03/2020: The course starts today! * 03/03/2020: The course is going to start soon ...
Contents
Course Aim & Organization
The objective of this course is to give an advanced presentation, i.e., a statistical perspective, of the techniques most used in artificial intelligence and machine learning for pattern recognition, knowledge discovery, and data analysis/modeling. The course will provide the basics of Regression, Classification, and Clustering with practical exercises using the Python language.
Teachers
The course is composed by a blending of lectures and exercises by the course teacher and a teaching assistant.
- Matteo Matteucci: the course teacher
- Marco Cannici: the teaching assistant
Course Program
Techniques from machine and statistical learning are presented from a theoretical (i.e., based on statistics and information theory) and practical perspective through the descriptions of algorithms, the theory behind them, their implementation issues, and few examples from real applications. The course mostly follows the following book which is also available for download in pdf
- An Introduction to Statistical Learning with Applications in R by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
Detailed course schedule
A detailed schedule of the course can be found here; topics are just indicative while days and teachers are correct up to some last minute change (I will notify you by email). Please note that not all days we have lectures!!
Note: Lecture timetable interpretation * On Wednesday, in room ..., starts at 014:15 (cum tempore), ends at 17:15 or 18:15 * On Thursday, in room ..., starts at 08:15 (cum tempore), ends at 10:15
| Date | Day | Time | Room | Teacher | Topic |
| 11/03/2020 | Wednesday | 14:30 - 17:30 | Teams Virtual Class | Matteo Matteucci | Course Introduction (Ch. 1 ISL) |
| 12/03/2020 | Thursday | 08:15 - 10:15 | Teams Virtual Class | Matteo Matteucci | Statistical Decision Theory and Bias-Variance trade off. (Ch. 2 ISL) |
Chapters are intended as complete except for
- Ch.4 ESL: Section 4.5
- Ch.12 ESL: Sections 12.1, 12.2, 12.3
- Ch.9 ISL: Sections 9.1, 9.2, 9.3
Course Evaluation
The course evaluation is composed by two parts:
- HW: Homework with exercises covering the whole program (up to 5 points)
- WE: A written examination covering the whole program (up to 27 points)
the final score will be the sum of HW (not compulsory) and WE scores.
